考虑复杂地形的越野环境无人车辆路径规划研究

When autonomous vehicles operate in off-road environments, they often face complex terrains and constantly changing road conditions. To realize reliable and efficient path planning and ensure the safe and maneuverable operation of the vehicles, a path planning method for off-road autonomous vehicles that takes into account complex terrains is proposed. The method consists of global path planning and trajectory planning. For global path planning, an improved Theta* algorithm based on rough terrain artificial potential fields is proposed. This algorithm considers factors such as slope, ground type, and elevation to keep the vehicle away from rough terrains. By reducing the slope and undulating terrains in the path, the efficiency, comfort, and safety of the vehicle in off-road environments are enhanced. Regarding local trajectory planning, an adaptive probabilistic roadmap method(APRM) algorithm is presented for handling dynamic driving scenarios. It utilizes different sampling strategies to adapt to the changing off-road driving conditions and obstacles. This enhances the efficiency of constructing the path network for complex off-road environments. Experimental verification shows that the improved Theta* algorithm reduces the average slope of the global path by 35.63% and decreases the surface undulation by 33.56%. The APRM algorithm reduces the time for local trajectory planning in unstructured roads and open terrains by 79.68% and 54.74%, respectively.